Multimorbidity is an emerging topic in public health policy because of its increasing prevalence and socio-economic impact. However, the age- and gender-dependent trends of disease associations at fine resolution, and the underlying genetic factors, remain incompletely understood. Here, by analyzing disease networks from electronic medical records of primary health care, we identify key conditions and shared genetic factors influencing multimorbidity. Three types of diseases are outlined: "central", which include chronic and non-chronic conditions, have higher cumulative risks of disease associations; "community roots" have lower cumulative risks, but inform on continuing clustered disease associations with age; and "seeds of bursts", which most are chronic, reveal outbreaks of disease associations leading to multimorbidity. The diseases with a major impact on multimorbidity are caused by genes that occupy central positions in the network of human disease genes. Alteration of lipid metabolism connects breast cancer, diabetic neuropathy and nutritional anemia. Evaluation of key disease associations by a genome-wide association study identifies shared genetic factors and further supports causal commonalities between nervous system diseases and nutritional anemias. This study also reveals many shared genetic signals with other diseases. Collectively, our results depict novel population-based multimorbidity patterns, identify key diseases within them, and highlight pleiotropy influencing multimorbidity.

Heritability estimates have revealed an important contribution of SNP variants for most common traits; however, SNP analysis by single-trait genomewide association studies (GWAS) has failed to uncover their impact. In this study, we applied a multitrait GWAS approach to discover additional factor of the missing heritability of human anthropometric variation.

We analysed 205 traits, including diseases identified at baseline in the GCAT cohort (Genomes For Life- Cohort study of the Genomes of Catalonia) (n=4988), a Mediterranean adult population-based cohort study from the south of Europe. We estimated SNP heritability contribution and single-trait GWAS for all traits from 15 million SNP variants. Then, we applied a multitraitrelated approach to study genome-wide association to anthropometric measures in a two-stage meta-analysis with the UK Biobank cohort (n=336 107).

The prevalence of chronic non-communicable diseases (NCDs) is increasing worldwide. NCDs are the leading cause of both morbidity and mortality, and it is estimated that by 2030, they will be responsible for 80% of deaths across the world. Therefore, it is important to design and develop new primary and secondary prevention strategies to reduce these exposures to a hazard risk factor. The Genomes for Life (GCAT) project is a long-term prospective cohort study that was designed to integrate and assess the role of epidemiologic, genomic, and epigenomic factors in the development of cancer and other major chronic diseases in Catalonia, a northeast region of Spain.

The hepatitis C virus (HCV) is a globally prevalent infectious pathogen. As many as 80% of people infected with HCV do not control the virus and develop a chronic infection. Response to interferon (IFN) therapy is widely variable in chronic HCV infected patients, suggesting that HCV has evolved mechanisms to suppress and evade innate immunity responsible for its control and elimination. Adenosine deaminase acting on RNA 1 (ADAR1) is a relevant factor in the regulation of the innate immune response. The loss of ADAR1 RNA-editing activity and the resulting loss of inosine bases in RNA are critical in producing aberrant RLR-mediated innate immune response, mediated by RNA sensors MDA5 and RIG-I. Here, we describe ADAR1 role as a regulator of innate and antiviral immune function in HCV infection, both in vitro and in patients. Polymorphisms within ADAR1 gene were found significantly associated to poor clinical outcome to HCV therapy and advanced liver fibrosis in a cohort of HCV and HIV-1 coinfected patients. Moreover, ADAR1 knockdown in primary macrophages and Huh7 hepatoma cells enhanced IFN and IFN stimulated gene expression and increased HCV replication in vitro. Overall, our results demonstrate that ADAR1 regulates innate immune signaling and is an important contributor to the outcome of the HCV virus-host interaction. ADAR1 is a potential target to boost antiviral immune response in HCV infection.

During the last decade, the interest to apply machine learning algorithms to genomic data has increased in many bioinformatics applications. Analyzing this type of data entails difficulties for managing high-dimensional data, class imbalance for knowledge extraction, identifying important features and classifying individuals. In this study, we propose a general framework to tackle these challenges with different machine learning algorithms and techniques. We apply the configuration of this framework on lung cancer patients, identifying genetic signatures for classifying response to drug treatment response. We intersect these relevant SNPs with the GWAS Catalog of the National Human Genome Research Institute and explore the Regulomedb, GTEx databases for functional analysis purposes.